3D Localization of RFID Tags Using SA Single-Antenna Based on Time Series Regression Model

With the rapid development of the Internet of Things (IoT), an increasing number of industrial demands have become urgent. Radio frequency identification (RFID) system plays a crucial role in addressing these challenges. It has now become a significant direction for the development of industrial automatic identification and data collection technology. However, the existing three-dimensional (3D) high-precision absolute localization methods have issues that need to be resolved. For example, the antenna sampling position needs to be used as known prior information, and the timestamp information of the samples is not fully utilized. Additionally, relying on multiple antennas or reference tags as auxiliary measures reduces the system flexibility. To overcome these challenges, we propose a single-antenna, multi-target, and 3D localization method based on an attention mechanism and neural network regression model inspired by the synthetic aperture (SA) method. This method suggests using a single antenna moving uniformly on a slider for continuous motion and sampling. By utilizing sample phase and time-domain information, it achieves 3D localization of cargo boxes in intelligent warehousing environments. In comparison with the current state-of-the-art localization solutions, our method doesn’t give the use of reference tags and utilizes a reduced number of antennas. The experimental results prove the feasibility of using only mobile antennas without reference tags to perform 3D multi-target positioning tasks. The combined dimensional average positioning error of 3D indoor positioning achieves a high-precision positioning, reaching 2.04cm and exhibits good robustness.